This is the official implementation of our NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition, by Hao Liu, Xinghua Jiang, Xin Li, Zhimin Bao, Deqiang Jiang and Bo Ren.

03/02/2022: NomMer got accepted by CVPR 2022.

We propose a novel ViT architecture, termed NomMer, which can dynamically Nominate the synergistic global-local context in vision transforMer.

To evaluate a pre-trained NomMer on ImageNet val, run:

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py --eval \
--cfg <config-file> --batch-size <batch-size-per-gpu> --resume <checkpoint> --data-path <imagenet-path>

To train a NomMer on ImageNet from scratch, run:

python -m torch.distributed.launch --nproc_per_node <num-of-gpus-to-use> --master_port 12345 main.py \
--cfg <config-file> --data-path <imagenet-path> --batch-size <batch-size-per-gpu> --output <output-directory>

If you find NomMer useful in your research, please consider citing:

@inproceedings{liu2022nommer,
    title={NomMer: Nominate Synergistic Context in Vision Transformer for Visual Recognition},
    author={Liu, Hao and Jiang, Xinghua and Li, Xin and Bao, Zhimin and Jiang, Deqiang and Ren, Bo},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
    year={2022}
}

Our codebase is built based on Swin-Transformer. We thank the authors for the nicely organized code!